Automatic Measurement of Postural Abnormalities With a Pose Estimation Algorithm in Parkinson’s Disease

18F-labeled fluoropropyl-carbomethoxylodopropyl-nor-ß-tropane ([18F]FP-CIT) positron emission tomography (PET) is a useful tool for evaluating disease progression in Parkinson's disease (PD) patients. We evaluated the test-retest reproducibility of [18F]FP-CIT PET measures in essential tremor (ET) and PD patients. Fifteen ET (68.9 ± 6.6years) and 10 PD patients (70.5 ± 6.3years; Hoehn and Yahr stage, 2.3 ± 0.8) underwent two [18F]FP-CIT PET/CT scans with an interval of 48 ± 7day. For both the test and retest studies, standardized uptake value ratios were estimated for 90-min and 3-h acquisitions for the caudate, anterior putamen, and posterior putamen using T1-MRI-based normalization (automatic) and fixed-VOI (manual) methods, with the occipital lobe as a reference. Reproducibility was evaluated by the bias, variability, percent test-retest, within-subject coefficient of variation, repeatability coefficient, and intraclass correlation coefficient (ICC). Reproducibility was excellent, with low variability (ET: 6.99-8.02%, PD: 3.51-6.94%) and high reliability (ICC; ET: 0.88-0.96, PD: 0.98-0.99). The ET group showed higher variability and lower ICCs than the PD group. The variability in the 90-min images (ET: 7.85-8.59%, PD: 1.52-2.75%) was comparable to that in the 3-h images (ET: 6.99-8.02%, PD: 3.51-6.94%). There were no differences in variability among the subregions in the ET group. In the PD group, the variability was high in the posterior putamen (automatic method: 6.94%, manual method: 11.80%). The test-retest variability and ICCs were similar for the manual and automatic methods. [18F]FP-CIT PET is reproducible for the quantitative measurement of DAT binding in both ET and PD individuals, independent of the acquisition time or analysis method. Also, the automaticmethod is more suitable for evaluating early loss of DAT binding inpatients with PD.

Postural abnormalities in Parkinson's disease (PD) patients and unimpaired elderly are not well differentiated. Factors related to postural abnormality associated with PD are controversial. We assessed differences in postural change between PD patients and unimpaired elderly and elucidated factors related to abnormal posture in PD patients. We measured the dropped head angle (DHA), anterior flexion angle (AFA), and lateral flexion angle (LFA) of the thoracolumbar spine of an unprecedented 1,117 PD patients and 2,732 general population participants (GPPs) using digital photographs. Two statistical analyses were used for elucidating factors related to these angles. In GPPs, age was correlated with DHA, AFA, and LFA. DHAs, AFAs, and LFAs of PD patients and age-matched GPPs were 21.70° ± 14.40° and 13.13° ± 10.79°, 5.98° ± 12.67,°and - 3.82° ± 4.04°, and 0.86° ± 4.25° and 1.33° ± 2.16°, respectively. In PD patients, factors related to DHA were age, male sex, and H & Y stage during ON time. Factors related to AFA were age, duration of disease, H & Y stage during ON and OFF times, pain, vertebral disease, and bending to the right. A factor related to LFA was AFA. DHA and AFA of GGPs correlated with age and were larger in PD patients than those with in GPPs. Some PD patients showed angles far beyond the normal distribution. Thus, factors associated with disease aggravation affected postural abnormality in PD patients.

Overactivity of muscles is thought to be involved in postural abnormalities (PA) in Parkinson's disease (PD). Here, we investigated the relationship between muscle activity and postural parameters to explore the peripheral mechanisms of PA. A total of 90 PD patients and 19 healthy controls were enrolled. Posture features (F1-F8) and the index for PA were collected. Surface electromyography was acquired from cervical and thoracolumbar muscles during lying, sitting, standing, and walking, respectively. Follow-up was completed for a subset of PD patients. Root mean square (RMS) amplitudes of the sternocleidomastoid muscle (SCM) and external oblique muscle, were higher in PD patients with PA (PD_PA) than PD patients without PA (PD_NPA) and healthy controls during lying and standing. Extensor activity in PD_PA patients increased only in the antigravity position compared with PD_NPA patients. Spearman's correlation showed that RMS amplitude of SCM in the lying position was associated with index for PA and forward flexion angles including F3-F8, whereas RMS amplitude of the external oblique muscle in the lying position was correlated with F5 alone. Longitudinal analysis showed that changes in F3, F4, and index for PA were significantly correlated with changes in RMS amplitude of the SCM in the lying position. Additionally, PD_NPA patients with SCM overactivity had a significantly higher risk of progressing to PA than those with normal SCM activity. The SCM and external oblique muscle are involved in forward trunk flexion in PD patients, while extensors may play a compensatory role. SCM may be the key muscle in PA for PD patients, participating in the pathogenesis of PA. ANN NEUROL 2025;98:967-976.

Axial Postural Abnormalities in Parkinsonism: Gaps in Predictors, Pathophysiology, and Management.

BACKGROUND. Estimation of fractional flow reserve from coronary CTA (FFR-CT) is an established method of assessing the hemodynamic significance of coronary lesions. However, clinical implementation has progressed slowly, partly because of off-site data transfer with long turnaround times for results. OBJECTIVE. The purpose of this study was to evaluate the diagnostic performance of FFR-CT computed on-site with a high-speed deep learning-based algorithm with invasive hemodynamic indexes as the reference standard. METHODS. This retrospective study included 59 patients (46 men, 13 women; mean age, 66.5 ± 10.2 years) who underwent coronary CTA (including calcium scoring) followed within 90 days by invasive angiography with invasive fractional flow reserve (FFR) and/or instantaneous wave-free ratio measurements from December 2014 to October 2021. Coronary artery lesions were considered to have hemodynamically significant stenosis in the presence of invasive FFR of 0.80 or less and/or instantaneous wave-free ratio of 0.89 or less. A single cardiologist evaluated the CTA images using an on-site deep learning-based semiautomated algorithm entailing a 3D computational flow dynamics model to determine FFR-CT for coronary artery lesions detected with invasive angiography. Time for FFR-CT analysis was recorded. FFR-CT analysis was repeated by the same cardiologist in 26 randomly selected examinations and by a different cardiologist in 45 randomly selected examinations. Diagnostic performance and agreement were assessed. RESULTS. A total of 74 lesions were identified with invasive angiography. FFR-CT and invasive FFR had strong correlation (r = 0.81) and, in Bland-Altman analysis, bias of 0.01 and 95% limits of agreement of -0.13 to 0.15. FFR-CT had AUC for hemodynamically significant stenosis of 0.975. At a cutoff of 0.80 or less, FFR-CT had 95.9% accuracy, 93.5% sensitivity, and 97.7% specificity. In 39 lesions with severe calcifications (≥ 400 Agatston units), FFR-CT had AUC of 0.991 and at a cutoff of 0.80, 94.7% sensitivity, 95.0% specificity, and 94.9% accuracy. Mean analysis time per patient was 7 minutes 54 seconds. Intraobserver agreement (intraclass correlation coefficient, 0.85; bias, -0.01; 95% limits of agreement, -0.12 and 0.10) and interobserver agreement (intraclass correlation coefficient, 0.94; bias, -0.01; 95% limits of agreement, -0.08 and 0.07) were good to excellent. CONCLUSION. A high-speed on-site deep learning-based FFR-CT algorithm had excellent diagnostic performance for hemodynamically significant stenosis with high reproducibility. CLINICAL IMPACT. The algorithm should facilitate implementation of FFR-CT technology into routine clinical practice.

Software-based measurements of axial postural abnormalities in Parkinson's disease (PD) are the gold standard but may be time-consuming and not always feasible in clinical practice. An automatic and reliable software to accurately obtain real-time spine flexion angles according to the recently proposed consensus-based criteria would be a useful tool for both research and clinical practice. We aimed to develop and validate a new software based on Deep Neural Networks to perform automatic measures of PD axial postural abnormalities. A total of 76 pictures from 55 PD patients with different degrees of anterior and lateral trunk flexion were used for the development and pilot validation of a new software called AutoPosturePD (APP); postural abnormalities were measured in lateral and posterior view using the freeware NeuroPostureApp (gold standard) and compared with the automatic measurement provided by the APP. Sensitivity and specificity for the diagnosis of camptocormia and Pisa syndrome were assessed. We found an excellent agreement between the new APP and the gold standard for lateral trunk flexion (intraclass correlation coefficient [ICC] 0.960, IC95% 0.913-0.982, P < 0.001), anterior trunk flexion with thoracic fulcrum (ICC 0.929, IC95% 0.846-0.968, P < 0.001) and anterior trunk flexion with lumbar fulcrum (ICC 0.991, IC95% 0.962-0.997, P < 0.001). Sensitivity and specificity were 100% and 100% for detecting Pisa syndrome, 100% and 95.5% for camptocormia with thoracic fulcrum, 100% and 80.9% for camptocormia with lumbar fulcrum. AutoPosturePD is a valid tool for spine flexion measurement in PD, accurately supporting the diagnosis of Pisa syndrome and camptocormia.

Gait and axial postural abnormalities correlations in Parkinson's disease: A multicenter quantitative study

Postural abnormality (PA) is a key motor symptom in Parkinson's disease (PD) that leads to disability and death. However, the pathophysiology underlying PA is still unknown. The objective of this study was to explore the neural patterns behind PAs and measures toward functional restoration using repetitive transcranial magnetic stimulation (TMS) in PD. We included cross-sectional (n = 181) and TMS-based therapy-response (n = 45) analyses of PD. Posture features, structural magnetic resonance imaging (MRI), and functional MRI data were collected. After regressing out covariates, brain maps of morphometry image and functional MRI data were compared to determine stimulation targets for the subsequent TMS sessions. Spearman's correlation analysis was conducted to explore relationships between posture features and MRI data. PD patients with PA (PD_PA) showed a greater extent of brain atrophy and functional alterations in bilateral lingual gyri and precentral gyri than patients without PA (PD_NPA). Furthermore, the PD_PA group had decreased functional connectivity between the left lingual gyrus and the left precentral gyrus compared with the PD_NPA group. The degree of such alteration inversely correlated with posture features. After 10 sessions of excitatory neuromodulation over the bilateral lingual gyri and precentral gyri, the Verum TMS group showed significant improvement in PA. We also observed increased degree centrality of the left lingual gyrus and enhanced functional connectivity between the left lingual gyrus and left inferior parietal lobule. Those changes were correlated with the improvement of PAs. Our study identifies a specific visual-motor integration circuitry involved in PAs of PD, where precentral gyrus and lingual gyrus are core nodes. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

There is a high prevalence of mild cognitive impairment (MCI) and dementia in Parkinson's disease (PD) patients, but a Chinese version of cognitive rating scale that is specific and sensitive to PD patients is still lacking. The aims of this study are to test the reliability and validity of a Chinese version of Parkinson's disease-cognitive rating scale (PD-CRS), establish cutoff scores for diagnosis of Parkinson's disease dementia (PDD) and PD with mild cognitive impairment (PD-MCI), explore cognitive profiles of PD-MCI and PDD, and find cognitive deficits suggesting a transition from PD-MCI to PDD. PD-CRS was revised based on the culture background of Chinese people. Ninety-two PD patients were recruited in three PD centers and were classified into PD with normal cognitive function (PD-NC), PD-MCI, and PDD subgroups according to the cognitive rating scale (CDR). Those PD patients underwent PD-CRS blind assessment by a separate neurologist. The PD-CRS showed a high internal consistency (Cronbach's Alpha = 0.840). Intraclass Correlation coefficient (ICC) of test-retest reliability reached 0.906 (95% CI 0.860–0.935, p < 0.001). ICC of inter-rater reliability was 0.899 (95% CI 0.848–0.933, p < 0.001). PD-CRS had fair concurrent validity with MDRS (ICC = 0.731, 95% CI 0.602–0.816). All the frontal-subcortical items showed significant decrease in PD-MCI compared with the PD-NC group (p ≤ 0.001), but the instrument cortical items did not (confrontation naming p=0.717, copying a clock p=0.620). All the frontal-subcortical and instrumental-cortical functions showed significant decline in PDD compared with the PD-NC group (p ≤ 0.001). The cutoff value for diagnosis of PD-MCI is 80.5 with the sensitivity of 75.7% and the specificity of 75.0%, and for diagnosis of PDD is 73.5 with the sensitivity of 89.2% and the specificity of 98.9%. Revised Chinese version of PD-CRS is a reliable, acceptable, valid, and useful neuropsychological battery for assessing cognition in PD patients.

Automated assessment of the substantia nigra on susceptibility map-weighted imaging using deep convolutional neural networks for diagnosis of Idiopathic Parkinson's disease

Improper endotracheal tube (ETT) positioning is frequently observed and potentially hazardous in the intensive care unit. The authors developed a deep learning-based automatic detection algorithm detecting the ETT tip and carina on portable supine chest radiographs to measure the ETT-carina distance. This study investigated the hypothesis that the algorithm might be more accurate than frontline critical care clinicians in ETT tip detection, carina detection, and ETT-carina distance measurement. A deep learning-based automatic detection algorithm was developed using 1,842 portable supine chest radiographs of 1,842 adult intubated patients, where two board-certified intensivists worked together to annotate the distal ETT end and tracheal bifurcation. The performance of the deep learning-based algorithm was assessed in 4-fold cross-validation (1,842 radiographs), external validation (216 radiographs), and an observer performance test (462 radiographs) involving 11 critical care clinicians. The performance metrics included the errors from the ground truth in ETT tip detection, carina detection, and ETT-carina distance measurement. During 4-fold cross-validation and external validation, the median errors (interquartile range) of the algorithm in ETT-carina distance measurement were 3.9 (1.8 to 7.1) mm and 4.2 (1.7 to 7.8) mm, respectively. During the observer performance test, the median errors (interquartile range) of the algorithm were 2.6 (1.6 to 4.8) mm, 3.6 (2.1 to 5.9) mm, and 4.0 (1.7 to 7.2) mm in ETT tip detection, carina detection, and ETT-carina distance measurement, significantly superior to that of 6, 10, and 7 clinicians (all P < 0.05), respectively. The algorithm outperformed 7, 3, and 0, 9, 6, and 4, and 5, 5, and 3 clinicians (all P < 0.005) regarding the proportions of chest radiographs within 5 mm, 10 mm, and 15 mm error in ETT tip detection, carina detection, and ETT-carina distance measurement, respectively. No clinician was significantly more accurate than the algorithm in any comparison. A deep learning-based algorithm can match or even outperform frontline critical care clinicians in ETT tip detection, carina detection, and ETT-carina distance measurement.

Objective: Parkinson's disease (PD) patients are known to suffer from pain, anxiety, and depression, but the exact degree of association between the two is unknown. As many PD patients also suffer from physical impairments, this cross-sectional case-control study sets out to compare and determine the case-ness of pain, anxiety and depression in PD patients that suffer with or without symptomatic osteoarthritis (OA). The goal of this study, therefore, was to observe if additional pain associated with comorbid OA in PD patients is correlated with greater depression and anxiety rates. The importance of understanding the burden of pain and increased depression severity of PD and OA patients is so that they may be screened appropriately based on the symptoms, which may increase their overall quality of life.Methods:This cross-sectional case-control study included 3 groups of 34 patients and 78 healthy age and gender-matched control participants. PD patients with symptomatic OA (PD+OA), PD patients without symptomatic OA (PD), patients with symptomatic OA but no PD (OA), and healthy control participants (Control). A PD patient group with Restless Legs Syndrome (PD+RLS) of 27 patients was also included. All participants completed questionnaires to assess for pain, depression, and anxiety.Results:PD+OA and PD patients had worsened depression severity and were more likely to report anxiety and depression case-ness than OA patients. PD+OA patients were more likely to complain about paresthestic and akasthisic pain, but less likely to complain about aching pain compared to PD patients and OA patients. PD+OA patients were more likely to have greater pain severity, and were more likely to report radiating and sharp pain than PD+RLS patients. PD+OA patients were also more likely to report higher depression case-ness than PD+RLS patients.Conclusion:PD with OA seems to be linked with specific pain characteristics (akathisia and paraesthesia) as well as heightened overall pain severity and pain interference in comparison to OA alone, PD alone and PD with RLS. PD is also correlated with depression severity and anxiety case-ness in OA when compared to the OA alone, PD alone and PD with RLS.

Blood biomarkers associated with cognitive decline in early stage and drug-naive Parkinson's disease patients

The overall frequency of postural abnormalities (PA) in Parkinson's disease (PD) is unknown. We evaluated the overall prevalence of PA and assessed the association with demographic and clinical variables. For this multicenter, cross-sectional study, consecutive PD outpatients attending 7 tertiary Italian centers were enrolled. Patients were evaluated and compared for the presence of isolated PA such as camptocormia, Pisa syndrome, and anterocollis and for combined forms (ie, camptocormia + Pisa syndrome) together with demographic and clinical variables. Of the total 811 PD patients enrolled, 174 (21.5%; 95% confidence interval [CI], 18.6%-24.3%) presented PA, 144 of which had isolated PA and 30 had combined PA. The prevalence of camptocormia was 11.2% (95% CI, 9%-13.3%), Pisa syndrome 8% (95% CI, 6.2%-9.9%), and anterocollis 6.5% (95% CI, 4.9%-8.3%). Patients with PA were more often male and older with longer disease duration, more advanced disease stage, more severe PD symptoms, a bradykinetic/rigid phenotype, and poorer quality of life. They were initially treated with levodopa, and more likely to be treated with a combination of levodopa and dopamine agonist, took a higher daily levodopa equivalent daily dose, and had more comorbidities. Falls and back pain were more frequent in PD patients with PA than in those without PA. Multiple logistic regression models confirmed an association between PA and male gender, older age, Hoehn and Yahr stage, and total Unified Parkinson's Disease Rating Scale score. PA are frequent and disabling complications in PD, especially in the advanced disease stages.

Alberta Stroke Program Early CT Score Calculation Using the Deep Learning-Based Brain Hemisphere Comparison Algorithm